TiFA: An Efficient and Robust LSPIV Algorithm Based on Joint
Distribution Analysis
Abstract
The incapability of processing flow velocities under low tracer density
conditions is one of the limitations of the traditional Large-Scale
Particle Image Velocimetry (LSPIV). This study developed a new LSPIV
algorithm, Time Frequency Analysis (TiFA), to overcome such a
limitation, enhance computational efficiency, and improve the accuracy
of derived velocities. TiFA investigates the temporal joint distribution
pattern of two velocity components at each location. By assuming that
the valid velocities follow a quasi-normal distribution in the velocity
time series, TiFA can quickly and accurately separate the valid
velocities from background noise and outliers. The performance of TiFA
was evaluated by comparing with other algorithms including Traditional
LSPIV, Ensemble Correlation (EC), Large-Scale Particle Tracking
Velocimetry (LSPTV), and Seeding Density Index (SDI) in an experimental
hydraulic model and two field cases. TiFA showed the highest overall
accuracy and lowest computation cost in data analysis, especially under
low tracer density conditions. In addition, TiFA showed its unique
ability of automatically filtering out velocity data from low-quality
zones such as no-tracer zones and surface glare zones. TiFA also showed
its potential in processing turbulent flow. In summary, the
newly-developed algorithm, TiFA, has demonstrated strong capability and
competence in various flow and tracer scenarios, making it a valuable
candidate for future applications.